{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.stats import gamma, norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu_0 = 200\n",
    "zeta_0 = 1\n",
    "alpha_0 = 1\n",
    "beta_0 = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_lambda_dist(alpha, beta):\n",
    "    return gamma(alpha, scale=1/beta)\n",
    "\n",
    "def gen_mu_dist(mu_mean, zeta, lmd):\n",
    "    return norm(loc=mu_mean, scale=np.sqrt(1/(zeta*lmd)))\n",
    "\n",
    "def draw(pdf, range_min, range_max, step):\n",
    "    xs = np.arange(range_min, range_max, step)\n",
    "    ys = [pdf.pdf(x) for x in xs]\n",
    "    plt.plot(xs, ys)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lambda_dist = gen_lambda_dist(alpha_0, beta_0) \n",
    "draw(lambda_dist, 0, 5, 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lmd = lambda_dist.rvs()                                            ###gaussgamma1mu\n",
    "mu_dist = gen_mu_dist(mu_0, zeta_0, lmd)\n",
    "draw(mu_dist, 180, 220, 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "data  = pd.read_csv(\"sensor_data_200.txt\", delimiter=\" \", \n",
    "                    header=None, names = (\"date\",\"time\",\"ir\",\"lidar\"))\n",
    "lidar = data[\"lidar\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "平均:  nan\n",
      "標準偏差:  nan\n"
     ]
    }
   ],
   "source": [
    "samples = lidar.sample(0)\n",
    "print(samples.values)\n",
    "print(\"平均: \", samples.mean())\n",
    "print(\"標準偏差: \", samples.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[200.0, 1, 1.0, 2.0]\n"
     ]
    }
   ],
   "source": [
    "N = len(samples)\n",
    "mu_N = 1.0/(N+beta_0)*sum(samples) + beta_0/(N+beta_0)*mu_0\n",
    "zeta_N = N + zeta_0\n",
    "alpha_N = N/2 + alpha_0\n",
    "beta_N = 0.5*(sum([z**2 for z in samples]) + zeta_0*(mu_0**2) - zeta_N*(mu_N**2)) + beta_0\n",
    "print([mu_N, zeta_N, alpha_N, beta_N])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lambda_dist = gen_lambda_dist(alpha_N, beta_N) \n",
    "draw(lambda_dist, 0, 5, 0.01)\n",
    "draw(lambda_dist, 0, 0.01, 0.00001) #範囲・縮尺を変えたもの"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "平均:  200.0\n",
      "標準偏差:  1.0782819348840917\n"
     ]
    }
   ],
   "source": [
    "lmd = lambda_dist.rvs()\n",
    "mu_dist = gen_mu_dist(mu_N, zeta_N, lmd)\n",
    "draw(mu_dist, 180, 220, 0.1)\n",
    "\n",
    "print(\"平均: \", mu_dist.mean())\n",
    "print(\"標準偏差: \", np.sqrt(1/lmd))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "\n",
    "zs = np.arange(190,230, 0.1)\n",
    "ys = [norm.pdf(z, mu_dist.mean(), np.sqrt(1/lmd)) for z in zs]\n",
    "\n",
    "plt.plot(zs,ys)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
